Data Mining: Next Generation Challenges and Future Directions

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Hillol Kargupta
AAAI, 2004 - Computers - 558 pages
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Data mining, or knowledge discovery, has become an indispensable technology for businesses and researchers in many fields. Drawing on work in such areas as statistics, machine learning, pattern recognition, databases, and high performance computing, data mining extracts useful information from the large data sets now available to industry and science. This collection surveys the most recent advances in the field and charts directions for future research.The first part looks at pervasive, distributed, and stream data mining, discussing topics that include distributed data mining algorithms for new application areas, several aspects of next-generation data mining systems and applications, and detection of recurrent patterns in digital media. The second part considers data mining, counter-terrorism, and privacy concerns, examining such topics as biosurveillance, marshalling evidence through data mining, and link discovery. The third part looks at scientific data mining; topics include mining temporally-varying phenomena, data sets using graphs, and spatial data mining. The last part considers web, semantics, and data mining, examining advances in text mining algorithms and software, semantic webs, and other subjects.

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Existential Pleasures of Distributed Data Mining
Research Issues in Mining and Monitoring of Intelligence Data
A Consensus Framework for Integrating Distributed Clusterings

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About the author (2004)

Anupam Joshi is Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County.

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